There have been proposed many techniques for filtering video image data in order to detect significant features of the image represented by the image data. Writings in this feature include the following: M. P. Cagigal, et al., "Object movement characterization from low-light-level images," Optical Engineering, Aug. 1994. vol. 33, no. 8, pp. 2810-2812; S. J. Nowlan, et al., "Filter selection model for motion segmentation and velocity integration," J. Opt. Soc. Am A, Dec. 1994, vol 11, no. 12, pp. 3177-3200; T. G. Allen, et al., "Multiscale approaches to moving target detection in image sequences," Optical Engineering, Jul. 1994, vol. 33, no. 7, pp. 2248-2254; M.-P. Dubuisson, et al., "Contour Extraction of Moving Objects in Complex Outdoor Scenes," International Journal of Computer Vision, 14, pp. 83-105 (1995); M. Bichsel, "Segmenting Simply Connected Moving Objects in a Static Scene," IEEE Transactions on Pattern Analysis and Machine Intelligence, Nov. 1994, vol. 16, no. 11, pp. 1138-1142; M. Irani, et al., "Computing Occluding and Transparent Motions," International Journal of Computer Vision, 12:1, pp. 5-16 (1994); I. J. Cox, "A Review of Statistical Data Association Techniques for Motion Correspondence," International Journal of Computer Vision, 10:1, pp. 53-66 (1993); D. Koller, et al., "Model-Based Object Tracking in Monocular Image Sequences of Road Traffic Scenes," International Journal of Computer Vision, 10:3, pp. 257-281 (1993); J. H. Lee, et al., "A VLSI Chip for motion estimation of HDTV Signals," IEEE Transactions on Consumer Electronics, May 1994, vol. 40, no. 2, pp. 154-160; T. Koivunen, "Motion detection of an interlaced video signal," IEEE Transactions on Consumer Electronics, Aug. 1994, vol. 40, no. 3, pp. 753-760; S.-I. Jang, et al., "A real-time identification method on motion and out-of-focus blur for a video camera," IEEE Transactions on Consumer Electronics, May 1994, vol. 40, no. 2, pp. 145-153. However, commercial applications of these proposed filtering techniques have been rare, and for the most part have been limited to analysis of static images, rather than attempting to acquire information by analyzing a dynamic stream of images.
A product which makes significant use of information obtained by analysis of a stream of video images is the OPTIMA II video surveillance multiplexer introduced by the assignee of the present application. The OPTIMA II multiplexer receives a respective stream of video image information from each of a plurality of cameras and forms a combined stream of images by time-division multiplexing of the images from the cameras. The combined stream is then output to a conventional video tape recorder for storage on tape. The OPTIMA II multiplexer applies motion detection filtering to the respective input streams and adaptively allocates the "time slots" in the output stream by allocating a larger number of slots to images from an input stream in which motion is detected. In this way, an input stream having a relatively large amount of information is allocated a relatively large portion of the system's storage capacity.
Other proposals to provide automatic response to detected features of video information have been directed primarily to detecting motion and to actuating an alarm condition when motion is detected. Such proposals are disclosed in U.S. Pat. Nos. 4,737,847 (Araki et al.); 4,160,998 (Kamin); 4,198,653 (Kamin); 3,988,533 (Mick et al.); 4,081,830 (Mick et al.); 3,743,768 (Copeland); and 4,249,207 (Harmon et al.).
It would be desirable to extend application of machine intelligence to detection of video information features, and automatic performance of functions, beyond those contemplated in the prior art.